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bioRxiv · 10.1101/2021.10.25.465745

Hi-C Resolution Enhancement with Genome Sequence Data

Kriukov, D.·Koritskiy, N.·Kozlovskii, I.·Zaretckii, M.·Bazarevich, M.·Zybin, M.·Khrameeva, E.
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Abstract

The increasing interest in chromatin conformation inside the nucleus and the availability of genome-wide experimental data make it possible to develop computational methods that can increase the quality of the data and thus overcome the limitations of high experimental costs. Here we develop a deep-learning approach for increasing Hi-C data resolution by appending additional information about genome sequence. In this approach, we utilize two different deep-learning algorithms: the image-to-image model, which enhances Hi-C resolution by itself, and the sequence-to-image model, which uses additional information about the underlying genome sequence for further resolution improvement. Both models are combined with the simple head model that provides a more accurate enhancement of initial low-resolution Hi-C data. The code is freely available in a GitHub repository: https://github.com/koritsky/DL2021_HI-C.

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Kriukov, D., Koritskiy, N., Kozlovskii, I., Zaretckii, M., Bazarevich, M., Zybin, M., Khrameeva, E.. 2021-10-26. Hi-C Resolution Enhancement with Genome Sequence Data. https://doi.org/10.1101/2021.10.25.465745

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